Who is better suited for an AI product management role compared to a traditional pm role?

This is not a simple question. There is no such thing as a "traditional PM" because PM roles and responsibilities differ by industry segment. There are substantial differences in PM roles from consumer app to enterprise app to hardware and enterprise infrastructure. 

Fundamentally the role of the PM is still the same - to consider customer problems, potential solution approaches, and lead them from concept to launch and facilitate a continous innovation lifecycle. The only difference here in AI/ML PM is a new approch to solving the problem with AI/ML tools and frameworks. So anyone with a "Learning mindset", ability to pick up on the AI/ML technologies and comfortable in ambiguity as this field is still growing, -- would do very well. 

5 years from now, likely there is going to be no difference between a Traditional PM and AI PM. AI is going to be used/present in all products. I see "Traditional PM role" as a foundational one to have, upon which one can grow to become a good AI PM. Good Traditional PM with aptitude for tech and data science is likely to do well as a good AI PM. Taking a Udemy course on basics of AI/ML, and applying to every day PM job will be a great start.

I've often said that PM is misunderstood as a relatively junior and technical job. It's actually best when it's treated as a strategic function, and being technical is a bonus but not necessarily the be all end all.

I do think AI PM might be a bit of an exception to the last piece. You need to understand the enabling technology a bit better as an AI PM than as a PM in other domains. So I'd say you need to have the desire an ability to get into the weeds a bit. To be clear, the intent here is in no way to trample on the authority of you engineering counterparts. Always let them own the "how." But being fluent in AI will let you have much more meaningful technical tradeoff conversations.

As for where to learn, I learn best by doing. I worked my way through a book on ML models in python several years ago and had a blast and got a better sense of the art of the possible. If you're the type that learns better in structured learning there are great courses online or at local universities.

Overall I believe that all PMs can be AI PMs since AI PMs also have to be equally customer obsessed as traditional PMs. However, I do believe AI product management role can be a bit different than a traditional PM role. Some differentiations and questions to think about it?

➡ Technical Understanding: Can you explain how a machine learning model works, in layman's terms? Or, how would you handle the situation where the model is giving unpredictable outputs?

➡ Data Management: Can you provide an example of a project where the quality or type of data significantly affected the outcome?

➡ Adaptability and Problem-solving: Can you tell about a time when an AI project you were working on didn't go as planned. How did you pivot?

➡ User-Centric Approach: How do you ensure that AI-driven decisions are transparent and understandable to users? How do you ensure users get a great experience even when AI does not work well

➡ Regulatory Knowledge: How do you stay updated with the regulatory changes in AI? How do you overcome those barriers and find a path forward?

➡ Communication and Collaboration: How do you bridge the gap between technical (data scientists, AI engineers) and non-technical (sales, marketing, customer service) teams in the development and deployment of AI products?

These give you a color of what nuances exist for being a great AI PM

Both AI product managers and traditional product managers play crucial roles in guiding the development and success of products, but there are certain qualities and skills that may make someone better suited for an AI product management role:

1. Technical Background: AI product managers often deal with complex algorithms, machine learning models, and data-driven decision-making. A background in computer science, data science, or a related field can provide a deeper understanding of the technology behind AI products.

2. Data & AI/ML Knowledge: AI products rely heavily on data. A strong understanding of data analytics, statistical methods, and data visualization is essential for analyzing user behavior, training models, and deriving insights from data. Familiarity with artificial intelligence and machine learning concepts is crucial for AI product managers. They need to understand how AI algorithms work, their limitations, and potential biases to make informed decisions about AI-powered features and functionalities.

3. Ethical Considerations: AI product managers must consider ethical implications related to data privacy, fairness, transparency, and bias in AI algorithms. A strong sense of ethics and a commitment to responsible AI development are essential in this role.